SupportSage-TinyLlama-RAG is a specialized Retrieval-Augmented Generation (RAG) system built to optimize customer support workflows. It leverages the TinyLlama-1.1B architecture to provide high-accuracy, context-aware responses based on internal documentation and ticket history.

Model Details

Developed by: Komron Khayumov
Model type: RAG-enhanced Large Language Model
Language: English
Base Model: TinyLlama-1.1B
Frameworks: LangChain, ChromaDB, and Streamlit

Intended Use

This model is designed for enterprise-level customer service automation. It is intended to be used in conjunction with a vector database (ChromaDB) to retrieve relevant context before generating answers, significantly reducing model hallucinations.

Training Data & Methodology

Data Source: Tested and validated on 61,700 real-world customer support tickets.
Workflow: The system uses semantic search to fetch relevant tickets from a ChromaDB vector store before passing the context to the LLM.

Performance Metrics

System Accuracy: 77.11%
Inference Speed: Highly optimized for real-time customer interactions using a small-footprint 1.1B parameter model.

How to Get Started

You can access the live demo of this model integrated into its UI here: SupportSage AI Space

Khayumov/SupportSage-TinyLlama-RAG

SupportSage-TinyLlama-RAG

Model Description

Model Details

Intended Use

Training Data & Methodology

Performance Metrics

How to Get Started

Khayumov/
SupportSage-TinyLlama-RAG